Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you'll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you'll learn about some of Silicon Valley's best practices in innovation as it pertains to machine learning and AI.
This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you'll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.

Bewertungen

SB

One of the best course at Coursera, the content are very well versed, assignments and quiz are quite challenging and good, Andrew is one of the best guide we could have in our side.\n\nThanks Coursera

SS

May 17, 2019

Filled StarFilled StarFilled StarFilled StarFilled Star

This is course just awesome. You get everything you wanted from this course. It covers on all topics in detail, helps in getting confidence in learning all the techiques and ideas in machine learning.

Aus der Unterrichtseinheit

Octave/Matlab Tutorial

This course includes programming assignments designed to help you understand how to implement the learning algorithms in practice. To complete the programming assignments, you will need to use Octave or MATLAB. This module introduces Octave/Matlab and shows you how to submit an assignment.

Unterrichtet von

Andrew Ng

Skript

You now know a bunch about machine learning. In this video, I like to teach you a programing language, Octave, in which you'll be able to very quickly implement the the learning algorithms we've seen already, and the learning algorithms we'll see later in this course. In the past, I've tried to teach machine learning using a large variety of different programming languages including C++ Java, Python, NumPy, and also Octave, and what I found was that students were able to learn the most productively learn the most quickly and prototype your algorithms most quickly using a relatively high level language like octave. In fact, what I often see in Silicon Valley is that if even if you need to build. If you want to build a large scale deployment of a learning algorithm, what people will often do is prototype and the language is Octave. Which is a great prototyping language. So you can sort of get your learning algorithms working quickly. And then only if you need to a very large scale deployment of it. Only then spend your time re-implementing the algorithm to C++ Java or some of the language like that. Because all the lessons we've learned is that a time or develop a time. That is your time. The machine learning's time is incredibly valuable. And if you can get your learning algorithms to work more quickly in Octave. Then overall you have a huge time savings by first developing the algorithms in Octave, and then implementing and maybe C++ Java, only after we have the ideas working. The most common prototyping language I see people use for machine learning are: Octave, MATLAB, Python, NumPy, and R. Octave is nice because open sourced. And MATLAB works well too, but it is expensive for to many people. But if you have access to a copy of MATLAB. You can also use MATLAB with this class. If you know Python, NumPy, or if you know R. I do see some people use it. But, what I see is that people usually end up developing somewhat more slowly, and you know, these languages. Because the Python, NumPy syntax is just slightly clunkier than the Octave syntax. And so because of that, and because we are releasing starter code in Octave. I strongly recommend that you not try to do the following exercises in this class in NumPy and R. But that I do recommend that you instead do the programming exercises for this class in octave instead. What I'm going to do in this video is go through a list of commands very, very quickly, and its goal is to quickly show you the range of commands and the range of things you can do in Octave. The course website will have a transcript of everything I do, and so after watching this video you can refer to the transcript posted on the course website when you want find a command. Concretely, what I recommend you do is first watch the tutorial videos. And after watching to the end, then install Octave on your computer. And finally, it goes to the course website, download the transcripts of the things you see in the session, and type in whatever commands seem interesting to you into Octave, so that it's running on your own computer, so you can see it run for yourself. And with that let's get started. Here's my Windows desktop, and I'm going to start up Octave. And I'm now in Octave. And that's my Octave prompt. Let me first show the elementary operations you can do in Octave. So you type in 5 + 6. That gives you the answer of 11. 3 - 2. 5 x 8, 1/2, 2^6 is 64. So those are the elementary math operations. You can also do logical operations. So one equals two. This evaluates to false. The percent command here means a comment. So, one equals two, evaluates to false. Which is represents by zero. One not equals to two. This is true. So that returns one. Note that a not equal sign is this tilde equals symbol. And not bang equals. Which is what some other programming languages use. Lets see logical operations one and zero use a double ampersand sign to the logical AND. And that evaluates false. One or zero is the OR operation. And that evaluates to true. And I can XOR one and zero, and that evaluates to one. This thing over on the left, this Octave 324.x equals 11, this is the default Octave prompt. It shows the, what, the version in Octave and so on. If you don't want that prompt, there's a somewhat cryptic command PF quote, greater than, greater than and so on, that you can use to change the prompt. And I guess this quote a string in the middle. Your quote, greater than, greater than, space. That's what I prefer my Octave prompt to look like. So if I hit enter. Oops, excuse me. Like so. PS1 like so. Now my Octave prompt has changed to the greater than, greater than sign.Which, you know, looks quite a bit better. Next let's talk about Octave variables. I can take the variable A and assign it to 3. And hit enter. And now A is equal to 3. You want to assign a variable, but you don't want to print out the result. If you put a semicolon, the semicolon suppresses the print output. So to do that, enter, it doesn't print anything. Whereas A equals 3. mix it, print it out, where A equals, 3 semicolon doesn't print anything. I can do string assignment. B equals hi Now if I just enter B it prints out the variable B. So B is the string hi C equals 3 greater than colon 1. So, now C evaluates the true. If you want to print out or display a variable, here's how you go about it. Let me set A equals Pi. And if I want to print A I can just type A like so, and it will print it out. For more complex printing there is also the DISP command which stands for Display. Display A just prints out A like so. You can also display strings so: DISP, sprintf, two decimals, percent 0.2, F, comma, A. Like so. And this will print out the string. Two decimals, colon, 3.14. This is kind of an old style C syntax. For those of you that have programmed C before, this is essentially the syntax you use to print screen. So the Sprintf generates a string that is less than the 2 decimals, 3.1 plus string. This percent 0.2 F means substitute A into here, showing the two digits after the decimal points. And DISP takes the string DISP generates it by the Sprintf command. Sprintf. The Sprintf command. And DISP actually displays the string. And to show you another example, Sprintf six decimals percent 0.6 F comma A. And, this should print Pi with six decimal places. Finally, I was saying, a like so, looks like this. There are useful shortcuts that type type formats long. It causes strings by default. Be displayed to a lot more decimal places. And format short is a command that restores the default of just printing a small number of digits. Okay, that's how you work with variables. Now let's look at vectors and matrices. Let's say I want to assign MAT A to the matrix. Let me show you an example: 1, 2, semicolon, 3, 4, semicolon, 5, 6. This generates a three by two matrix A whose first row is 1, 2. Second row 3, 4. Third row is 5, 6. What the semicolon does is essentially say, go to the next row of the matrix. There are other ways to type this in. Type A 1, 2 semicolon 3, 4, semicolon, 5, 6, like so. And that's another equivalent way of assigning A to be the values of this three by two matrix. Similarly you can assign vectors. So V equals 1, 2, 3. This is actually a row vector. Or this is a 3 by 1 vector. Where that is a fat Y vector, excuse me, not, this is a 1 by 3 matrix, right. Not 3 by 1. If I want to assign this to a column vector, what I would do instead is do v 1;2;3. And this will give me a 3 by 1. There's a 1 by 3 vector. So this will be a column vector. Here's some more useful notation. V equals 1: 0.1: 2. What this does is it sets V to the bunch of elements that start from 1. And increments and steps of 0.1 until you get up to 2. So if I do this, V is going to be this, you know, row vector. This is what one by eleven matrix really. That's 1, 1.1, 1.2, 1.3 and so on until we get up to two. Now, and I can also set V equals one colon six, and that sets V to be these numbers. 1 through 6, okay. Now here are some other ways to generate matrices. Ones 2.3 is a command that generates a matrix that is a two by three matrix that is the matrix of all ones. So if I set that c2 times ones two by three this generates a two by three matrix that is all two's. You can think of this as a shorter way of writing this and c2,2,2's and you can call them 2,2,2, which would also give you the same result. Let's say W equals one's, one by three, so this is going to be a row vector or a row of three one's and similarly you can also say w equals zeroes, one by three, and this generates a matrix. A one by three matrix of all zeros. Just a couple more ways to generate matrices . If I do W equals Rand one by three, this gives me a one by three matrix of all random numbers. If I do Rand three by three. This gives me a three by three matrix of all random numbers drawn from the uniform distribution between zero and one. So every time I do this, I get a different set of random numbers drawn uniformly between zero and one. For those of you that know what a Gaussian random variable is or for those of you that know what a normal random variable is, you can also set W equals Rand N, one by three. And so these are going to be three values drawn from a Gaussian distribution with mean zero and variance or standard deviation equal to one. And you can set more complex things like W equals minus six, plus the square root ten, times, lets say Rand N, one by ten thousand. And I'm going to put a semicolon at the end because I don't really want this printed out. This is going to be a what? Well, it's going to be a vector of, with a hundred thousand, excuse me, ten thousand elements. So, well, actually, you know what? Let's print it out. So this will generate a matrix like this. Right? With 10,000 elements. So that's what W is. And if I now plot a histogram of W with a hist command, I can now. And Octave's print hist command, you know, takes a couple seconds to bring this up, but this is a histogram of my random variable for W. There was minus 6 plus zero ten times this Gaussian random variable. And I can plot a histogram with more buckets, with more bins, with say, 50 bins. And this is my histogram of a Gaussian with mean minus 6. Because I have a minus 6 there plus square root 10 times this. So the variance of this Gaussian random variable is 10 on the standard deviation is square root of 10, which is about what? Three point one. Finally, one special command for generator matrix, which is the I command. So I stands for this is maybe a pun on the word identity. It's server set eye 4. This is the 4 by 4 identity matrix. So I equals eye 4. This gives me a 4 by 4 identity matrix. And I equals eye 5, eye 6. That gives me a 6 by 6 identity matrix, i3 is the 3 by 3 identity matrix. Lastly, to wrap up this video, there's one more useful command. Which is the help command. So you can type help i and this brings up the help function for the identity matrix. Hit Q to quit. And you can also type help rand. Brings up documentation for the rand or the random number generation function. Or even help help, which shows you, you know help on the help function. So, those are the basic operations in Octave. And with this you should be able to generate a few matrices, multiply, add things. And use the basic operations in Octave. In the next video, I'd like to start talking about more sophisticated commands and how to use data around and start to process data in Octave.